DIGITS

There are some wrappers around YOLO3 in Python which may help: https://github.com/qqwweee/keras-yolo3 It's not Dockerised though, but you could grab a container with the usual machine learning packages and install that on top. Also look at NVIDIA Digits (https://github.com/NVIDIA/DIGITS) which has Docker images.

For what I was using it for - locating fruit on a plant - detectnet works pretty well (it runs at 7fps on a TX2 at 1280x960). It's a modified GoogLeNet with some custom layer on the end to do the bounding boxes. DIGITS itself is just a management console. That said, you can train Tensorflow models using DIGITS, so I assume that DIGITS handles the data IO for you: https://github.com/NVIDIA/DIGITS/blob/master/docs/GettingSta... It looks like you just define a wrapper around your model so that DIGITS knows how to run it and evaluate the loss, but otherwise your model could be anything.

Oh, if you don't want to get into the nitty gritty of it, you should use Digits (https://github.com/NVIDIA/DIGITS). This is the easiest way to setup a CNN and train it with your sample images (at least compared to Caffe, Tensorflow, and Theano). I say that because it's all GUI based! Real convenient.